Target Detection in Agriculture Field by Eigenvector Reduction Method of Cem - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

Target Detection in Agriculture Field by Eigenvector Reduction Method of Cem

Abstract

Constrained Energy Minimization algorithm is used in hyperspectral remote sensing target detection, it only needs the spectrum of interest targets, knowledge of background is unnecessary, so it is well applied in hyperspectral remote sensing target detection. This paper analyzed the reason of better results in small target detections and worse ones in large target detections of CEM algorithm, an eigenvector reduction method to increase the ability of large target detection of CEM algorithm was proposed in this paper. Correlation matrix R was decomposed into eigenvalues and eigenvectors, then some eigenvectors corresponding to larger eigenvalues was choosed to reconstruct R. In order to test the effect of the new method, experiments are conducted on HYMAP hyperspectral remote sensing image. In conclusion, by using eigenvalue reduction method, the improved CEM method not only can detect large targets, but also can well detect large/small targets simultaneously.
Fichier principal
Vignette du fichier
0109--Chunhong_Liu.pdf (209.62 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01061714 , version 1 (08-09-2014)

Licence

Attribution

Identifiers

Cite

Chun-Hong Liu, Ping Li. Target Detection in Agriculture Field by Eigenvector Reduction Method of Cem. Third IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture III (CCTA), Oct 2009, Beijing, China. pp.1-7, ⟨10.1007/978-3-642-12220-0_1⟩. ⟨hal-01061714⟩
231 View
240 Download

Altmetric

Share

Gmail Facebook X LinkedIn More